Reinforcing RCTs with Multiple Priors while Learning about External Validity

Frederico Finan and Demian Pouzo

Papers from arXiv.org

Abstract: This paper introduces a framework for incorporating prior information into the design of sequential experiments. These sources may include past experiments, expert opinions, or the experimenter's intuition. We model the problem using a multi-prior Bayesian approach, mapping each source to a Bayesian model and aggregating them based on posterior probabilities. Policies are evaluated on three criteria: learning the parameters of payoff distributions, the probability of choosing the wrong treatment, and average rewards. Our framework demonstrates several desirable properties, including robustness to sources lacking external validity, while maintaining strong finite sample performance.

Date: 2021-12, Revised 2024-09
New Economics Papers: this item is included in nep-ecm, nep-exp and nep-gth
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Citations: View citations in EconPapers (2)

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